Data Contract

Our Data Contract Engine enables miners and OEMs to collaborate through trusted data, creating a win-win situation for ongoing mutual benefit and growth.

Why Data Contract Matters

With the growing number of systems and data sources in mining, data engineering and integration are becoming increasingly complex—making it costly, and often impossible, for miners to control data availability and quality.

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Analytics solutions are only successful if they deliver real value and generate a measurable return on investment.
Escalated integration costs
Loss of data quality control
Information overload
Misalignment
Misinterpretation
Loss of Trust

How StratDA Helps

  • Trusted Data-driven Collaboration
    Miners and OEMs collaborate through trusted data, creating a transparent and reliable foundation for partnership.
  • Mutual Growth and Benefits:
    Creating win-win scenarios where both miners and OEMs benefit from improved data sharing. Miners gain better operational insights, while OEMs receive valuable feedback to enhance their products and services.
  • Ecosystem-wide Innovation:
    Standardized data contract frameworks create an innovation ecosystem where data-driven best practices and operational improvements can be shared and scaled across the industry, benefiting all participants.
  • Enhanced Data Quality:
    Contracts establish data quality requirements—covering accuracy, completeness, timeliness, and consistency—to ensure operational needs are met.
  • Cost Optimisation:
    Standardized data integration reduces data engineering and maintenance costs. Mining companies can redirect resources from data management to value-creating analytics initiatives.
  • Strategic Partnerships:
    Data contracts turn vendor relationships into strategic partnerships, enabling collaborative innovation, joint solutions in data-driven mining operations, and shared investment in digital technologies.

How StratDA Works

Business Strategy Alignment

StratDA aligns business strategy, context, and value with data requirements by connecting data initiatives directly to organizational objectives and quantifying business impact to ensure measurable value delivery.

Problem-drive Data Specification

The engine starts with business problems and systematically derives the ideal input data required to address those specific challenges, ensuring data contracts are purpose-built to solve real operational issues.

Standardized Data Governance

StratDA maintains comprehensive, standardized data dictionaries that define common data categories, formats, and definitions across the mining industry, enabling broader data producers to easily understand requirements and contribute relevant information.
Geology Datasets
Machine Data
Fleet Management
Safety Data
Environment Data

Analytics and AI Use Cases

A structured approach to identifying, evaluating, prioritising, and managing analytics and AI use cases—ensuring alignment with business goals and the consistent delivery of measurable value across mining operations.

Report Design for Small Miners

Define the vision for business improvement and target data capability maturity aligned with mining goals—tailored to the business and operational context, and providing a benchmark to guide future planning and investment.

Proof of Concept

A lightweight, low-risk approach to rapidly test and validate analytics or AI ideas—demonstrating value, providing base scripts to accelerate development, and informing go/no-go decisions before full-scale implementation.

Data Contracts

A clear, agreed-upon specification between data producers and consumers—defining the structure, quality, ownership, and delivery expectations to ensure reliable, consistent, and trusted data for downstream use.

A clear, prioritised pipeline of high-impact analytics and AI initiatives

Strong alignment between technical solutions and business needs

Faster validation and value realisation from targeted use cases

Faster, more confident decisions at all levels

Improved operational focus through role-relevant insights

Greater trust in data through consistency and clarity

Reduced time spent searching for critical information

Clear evidence of potential value and feasibility

Refined requirements for smoother implementation

Reduced risk and cost before full-scale rollout

Faster stakeholder buy-in through tangible results

Improved data quality, reliability and consistency

Clear accountability between producers and consumers

Reduced errors and rework in downstream processes

Faster troubleshooting and issue resolution

Ready to Transform Your Business?

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